Today, we capture less than 1% of all emitted carbon dioxide, but in 2018, the Intergovernmental Panel on Climate Change (IPCC) reported that limiting atmospheric warming to less than 2 degrees Celsius will require both capture of carbon emissions from point sources such as power and industrial plants as well as the removal of existing atmospheric carbon dioxide. Amine solvents have gained the most traction in large-scale CCUS implementation, but limitations include high energy penalties associated with regeneration, cost, toxic degradation products, and corrosiveness. Improvements in stability, binding capacity, kinetics, and vapor-liquid equilibria have been achieved by blending different primary, secondary, and tertiary amines, but accelerating their discovery with machine learning and AI has seen limited exploration. We have built a parallelized assay platform for rapid testing and measurement of capacity, kinetics, and thermodynamics of CO2 binding to binary and tertiary blends of standard primary, secondary, and tertiary amines. Our platform replicates gas sparging of CO2 through liquid solvents and leverages NDIR sensors to measure CO2 absorption from complex mixtures of simulated flue gas. We also measure other physical parameters such as pKa, viscosity, and stability. With these data sets, we leverage machine learning to capture properties of novel blends and rank performance of these mixtures. New blends are tested and validated, and these data are delivered back to the model to improve its predictive capabilities with the end goal of generating new materials and solvent blends with improved performance for carbon capture.